1. Field of the Invention
The present invention relates to a defect image classification apparatus.
2. Description of the Related Art
In a semiconductor manufacturing process, to secure a high yield, it is important to discover a defect occurring in the manufacturing process early and take measures. A scanning electron microscope (SEM)-type defect observation device is a device to observe a defect occurred in the semiconductor manufacturing process, particularly. The SEM-type defect observation device is generally a device to observe an image of the defect coordinates detected by an upper (upstream side) defect inspection device with definition higher than definition of the upper defect inspection device. The upper defect inspection device is an optical defect inspection device, for example.
A specific flow in the SEM-type defect observation device will be described. The SEM-type defect observation device first moves a sample stage to the defect coordinates output by the upper defect inspection device and executes imaging with a low magnification of a degree where a defect to be observed enters a view. Next, the SEM-type defect observation device detects the defect coordinates from an imaged low-magnification image, moves the sample stage such that the defect is positioned at the center of the view or moves the center of the imaging, and acquires a high-magnification image for observation with the high magnification suitable for the defect observation. As such, the defect coordinates are detected from the low-magnification image before the high-magnification image for the observation is acquired, because an error is included in the defect coordinates output by the upper defect inspection device in a range of a device specification and processing for correcting the error is necessary when a high-definition defect image is acquired by the SEM-type defect observation device. Automating a process for acquiring the high-definition defect image (high-magnification image) is automatic defect review or automatic defect redetection (ADR).
Defect images acquired by the ADR or manually are classified for each kind of defects and are managed. Automating defect classification work is automatic defect classification (ADC). Different from the ADC, work for classifying the defect images manually is called manual defect classification (MDC). Classification performance of the ADC is improved. However, because the ADC cannot classify a wide variety of defects completely, the ADC is used together with the MDC in a large number of processes. Specifically, depending on the classification performance of the ADC, a classification result of the ADC is applied without a modification with respect to kinds of defects for which the classification performance of the ADC is sufficient, but work for confirming the classification result of the ADC by the MDC is performed with respect to kinds of defects for which the classification performance of the ADC is insufficient. When the confirmation work is performed by the MDC, a kind of a defect is specified by comparing a plurality of defect images existing with respect to one defect, for example, the low-magnification defect image, the high-magnification defect image, and a plurality of kinds of images detected by a plurality of detectors according to optical characteristics.
Meanwhile, JP-2012-83147-A discloses “a left image or a right image obtained by detecting backward scattered electrons in which it is easy to confirm an uneven situation is displayed when a classification result is a foreign material having a surface of a convex shape”.